Runtime Recovery Actions Selection for Sporadic Operations on Cloud

Abstract

Sporadic operations such as rolling upgrade or machine instance redeployment are prone to unpredictable failures in the cloud largely due to the inherent high variability nature of cloud. Previous dependability research has established several recovery methods for cloud failures. In this paper, we first propose eight recovery patterns for sporadic operations. We then present the filtering process which filters applicable recovery patterns for a given operational step. We also propose a methodology to evaluate the recovery actions generated for the applicable recovery patterns based on the metrics of Recovery Time, Recovery Cost and Recovery Impact. This quantitative evaluation will lead to selection of optimal recovery actions. We implement a recovery service and illustrate its applicability by recovering from errors occurring in Asgard rolling upgrade operation on cloud. The experimental results show that the recovery service enhances automated recovery from operational failures by selecting the optimal recovery actions.